Aspects of this invention relate to a computerized assessment and recommended intervention for high risk workers' compensation claims and, more specifically, to applying machine learning technologies to data and delivering results via electronic means over a computer network in an effort to identify and mitigate costs associated with high risk workers' compensation claims.
The American workers' compensation system is a no-fault scheme that has been implemented by each of the fifty states. While each state's law may vary from that of another state, all states share the same basic concepts: (1) if an employee is injured at work, the employer must provide defined benefits (medical, indemnity (lost wages) and disability) regardless of fault and (2) the employer is immune from a tort lawsuit by the employee on account of the employee's work injury. Generally, most states allow employers to insure their obligations to provide workers' compensation benefits through, for example, a primary insurance policy that provides first dollar coverage, a large deductible policy, a state managed fund, or a self-insurance program. Employers that are allowed to self-insure their workers' compensation liabilities are generally required to purchase some type of excess insurance that provides coverage for claims that exceed a self-insured retention (SIR).
As medical and pharmaceutical technology has advanced, so have the expenses associated with these treatments. In addition, medical cost inflation has also aggressively trended upward over the last several decades. It is well known that medical expenses per capita in the US far exceed other industrialized nations. Not unexpectedly, in the last decade, the primary expense associated with workers' compensation claims has dramatically shifted from lost wages to medical and pharmaceutical related expenses. Currently, medical and pharmacy expense make up, on average, 60% of the total costs in workers' compensation claims. Understanding these trends coupled with predicting and early intervention on high risk claims is paramount to tackling the problem of medical cost inflation and over utilization in workers' compensation claims.
Given the shift to medical related claim's expenses and the medical cost trends in the United States, workers' compensation insurers are acutely aware of the need to better manage claim's related medical costs. On complex claims, human adjusters must plow through voluminous medical records to understand the medical cost drivers on a claim and formulate a plan to mitigate claims costs. An automated approach to such review would drastically reduce time to identify and intervene on problem claims.
Workers' compensation claims can be classified into two broad categories—Medical Only and Indemnity claims. Medical only claims incur limited medical costs, no lost wage costs, and then close. Indemnity claims involve injuries that cause the employee to be out of work for a period of time. While most injured employees return to work, some do not. Those who do not return to work receive workers' compensation benefits for life or for a substantial period of time. Some injuries are catastrophic and are known to be high risk at claim outset. This class of claim would include injuries such as death, some amputations, serious burns, brain injuries and paralysis. These claims are assigned to only experienced adjusters and nurse case managers.
Although some claims are readily identifiable from the outset as high risk, a large percentage of high cost claims can be labeled as migratory claims. A migratory claim appears to be much like a normal risk claim and then medical conditions gradually worsen over time. For example, initially, a low back strain may result in lost time, limited medical treatment, and dispensing of pharmaceuticals. However, the normal claim can migrate to high risk if the claimant continues to experience pain and opts for a costly surgical intervention. In a typical migratory large loss claim, the claimant will have multiple surgeries over an extended period of time and likely end up on a cocktail of high-powered addictive drugs. Without a different medical treatment protocol, this pattern will repeat over and over incurring hundreds of thousands even millions of dollars in costs.
The workers' compensation insurance industry and, in particular, self-insured employers and their excess carriers, have been slow to adopt automation. While automation has occurred, most of it has centered on workflow for handling claims and sending alerts when red flags appear on a claim. Further, conventional approaches lack the ability to effectively predict migratory claims. At most, current implementations merely include rudimentary models based on the summation of red flags to generate a single risk score. An automated approach for the accurate and early identification of such claims and for suggesting intervention techniques would greatly improve existing manual processes to identify migratory claims.